Measured PET Data Characterization with the Negative Binomial Distribution Model

  • Maria Filomena Santarelli
  • Vincenzo Positano
  • Luigi Landini
Original Article

Abstract

Accurate statistical model of PET measurements is a prerequisite for a correct image reconstruction when using statistical image reconstruction algorithms, or when pre-filtering operations must be performed. Although radioactive decay follows a Poisson distribution, deviation from Poisson statistics occurs on projection data prior to reconstruction due to physical effects, measurement errors, correction of scatter and random coincidences. Modelling projection data can aid in understanding the statistical nature of the data in order to develop efficient processing methods and to reduce noise. This paper outlines the statistical behaviour of measured emission data evaluating the goodness of fit of the negative binomial (NB) distribution model to PET data for a wide range of emission activity values. An NB distribution model is characterized by the mean of the data and the dispersion parameter α that describes the deviation from Poisson statistics. Monte Carlo simulations were performed to evaluate: (a) the performances of the dispersion parameter α estimator, (b) the goodness of fit of the NB model for a wide range of activity values. We focused on the effect produced by correction for random and scatter events in the projection (sinogram) domain, due to their importance in quantitative analysis of PET data. The analysis developed herein allowed us to assess the accuracy of the NB distribution model to fit corrected sinogram data, and to evaluate the sensitivity of the dispersion parameter α to quantify deviation from Poisson statistics. By the sinogram ROI-based analysis, it was demonstrated that deviation on the measured data from Poisson statistics can be quantitatively characterized by the dispersion parameter α, in any noise conditions and corrections.

Keywords

Positron emission tomography (PET) Negative binomial (NB) distribution Maximum likelihood (ML) estimation Sinograms Poisson statistic deviation 

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Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  • Maria Filomena Santarelli
    • 1
    • 2
  • Vincenzo Positano
    • 2
  • Luigi Landini
    • 1
    • 2
    • 3
  1. 1.Institute of Clinical PhysiologyNational Research CouncilPisaItaly
  2. 2.Fondazione CNR-Regione Toscana “G. Monasterio”PisaItaly
  3. 3.Department of Information EngineeringUniversity of PisaPisaItaly

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